Senior Data Engineer

Tango; United States; On-site; USD 125,000 - 165,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive benefits

Flexible work environment

Inclusive culture

Job summary

Tango is seeking a Senior Data Engineer to join our Data & AI Engineering team. You will design, build, and maintain scalable ETL/ELT pipelines on AWS, owning the dbt transformation layer and ensuring data quality and governance across the data stack.

You will collaborate with application teams to design APIs, data contracts, and support analytics with curated datasets, while optimizing performance, cost, and observability. Flexible work arrangements are supported.

Qualifications

• 5+ years of professional experience as a Data Engineer or similar role.
• Strong expertise with Python and SQL, and modern data engineering frameworks.
• Deep experience with AWS data services (Glue, Lambda, Step Functions, S3, Redshift, RDS/Postgres).
• Hands-on experience with ETL/ELT pipeline design, orchestration, and performance tuning.
• Strong experience with production databases (Postgres, TimescaleDB, etc).
• Solid understanding of data modeling (OLTP, OLAP/star schema), warehousing, and analytics workloads.
• Experience with data quality frameworks, validation, and monitoring.
• Production experience with dbt : building and testing models, managing sources and lineage, structuring a project across staging/intermediate/mart layers and running it in CI.
• Experience with Git-based workflows and CI/CD for data: pull requests, code review and automated testing of transformations.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines using AWS-native services (Glue, Lambda, Step Functions, S3).
• Develop automated workflows to ingest, transform, and validate large volumes of structured and semi-structured data.
• Monitor pipeline performance, reliability, and cost efficiency; implement proactive improvements.
• Own the transformation layer in dbt : models, tests, sources, and documentation. Every dataset that reaches the warehouse is modeled in dbt and validated against source.
• Manage and optimize production databases (Postgres, TimescaleDB) and our OLAP data warehouse (Redshift, etc).
• Implement database performance tuning, indexing strategies, query optimizations, and schema evolution best practices.
• Oversee data retention, partitioning, and backup/restore strategies.
• Build automated data validation frameworks and anomaly detection , anchored in dbt tests and source-reconciliation checks.
• Establish and enforce data quality SLAs across ingestion and reporting layers.
• Maintain metadata, lineage, and documentation standards to support auditability (SOC 1/2, ISO).
• Work with application engineering teams to design APIs, microservices, and data contracts.
• Support Product and Data Analytics teams with curated datasets and high-performance query patterns.
• Troubleshoot production issues and improve observability using monitoring and alerting tools (CloudWatch, Datadog, etc).

Skills

Python

SQL

AWS

ETL/ELT

dbt

Git/CD

PostgreSQL

TimescaleDB

Data Modeling

Data Quality

Tools

AWS Glue

AWS Lambda

AWS Step Functions

Amazon S3

Redshift

RDS/Postgres

Job description

*Applicants must be authorized to work in the U.S. for any employer.

*We cannot sponsor employment-based visas at this time.

About The Company:

With hundreds of customers across more than 140 countries, Tango is the leader in cloud-based Software-as-a-Service (SaaS) solutions use to manage the end-to-end real estate and facilities lifecycles. Tango's Store Lifecycle Management and Integrated Workplace Management System software, deliver a single solution spanning real estate, design & construction, lease administration & accounting, facilities maintenance, occupancy management, energy & sustainability, desk booking, visitor and space management.

We are looking for aSenior Data Engineer to join our dynamic and growing Data & AI Engineering team.

Key Responsibilities :

• Design, build, and maintain scalable ETL/ELT pipelines using AWS-native services (Glue, Lambda, Step Functions, S3).
• Develop automated workflows to ingest, transform, and validate large volumes of structured and semi-structured data.
• Monitor pipeline performance, reliability, and cost efficiency; implement proactive improvements.
• Own the transformation layer in dbt : models, tests, sources, and documentation. Every dataset that reaches the warehouse is modeled in dbt and validated against source.

Database & Warehouse Management

• Manage and optimize production databases (Postgres, TimescaleDB) and our OLAP data warehouse (Redshift, etc).
• Implement database performance tuning, indexing strategies, query optimizations, and schema evolution best practices.
• Oversee data retention, partitioning, and backup/restore strategies.

Data Quality & Governance

• Build automated data validation frameworks and anomaly detection , anchored in dbt tests and source-reconciliation checks. .
• Establish and enforce data quality SLAs across ingestion and reporting layers.
• Maintain metadata, lineage, and documentation standards to support auditability (SOC 1/2, ISO).

Cross-Functional Collaboration

• Work with application engineering teams to design APIs, microservices, and data contracts.
• Support Product and Data Analytics teams with curated datasets and high-performance query patterns.
• Troubleshoot production issues and improve observability using monitoring and alerting tools (CloudWatch, Datadog, etc).

Required Skills:

• 5+ years of professional experience as a Data Engineer or similar role.
• Strong expertise with Python and SQL, and modern data engineering frameworks.
• Deep experience with AWS data services (Glue, Lambda, Step Functions, S3, Redshift, RDS/Postgres).
• Hands-on experience with ETL/ELT pipeline design, orchestration, and performance tuning.
• Strong experience with production databases (Postgres, TimescaleDB, etc).
• Solid understanding of data modeling (OLTP, OLAP/star schema), warehousing, and analytics workloads.
• Experience with data quality frameworks, validation, and monitoring.
• Production experience with dbt : building and testing models, managing sources and lineage, structuring a project across staging/intermediate/mart layers and running it in CI.
• Experience with Git-based workflows and CI/CD for data: pull requests, code review and automated testing of transformations.

Preferred:

• Experience with time-series data and high-volume ingestion pipelines.
• Background in the Energy, Sustainability, or IoT data domain.
• dbt Semantic Layer or comparable metrics-layer experience (Cube, LookML , Omni semantic models) .

What We Offer

We're committed to creating an environment where you can thrive - professionally and personally. Our offerings include:

• Competitive CompensationWe recognize and reward your contributions with a salary package that reflects your value.
• Comprehensive BenefitsIncluding health, dental, and vision insurance, a 401(k) plan with company match, and generous paid time off to support your well-being.
• Flexible Work EnvironmentWhether remote, hybrid, or in-office, we support work arrangements that promote productivity and balance.
• Inclusive & Collaborative CultureWe foster a workplace where diverse perspectives are valued, teamwork is encouraged, and everyone has a voice.

Tango is proud to be an equal opportunity employer. We are committed to equal opportunity regardless of race, ethnicity, religion, parental status, sexual orientation, age, citizenship, disability, or veteran status.

Basepayofferediscontingentonqualificationsandotheroperationalconsiderations.BasepayisjustonepieceofthefullcompensationstructureofferedatTango.Ifthispayrangeisoutsideofyourexpectations,westillencourageyoutoapplyandhaveaconversationwithus.

Base pay offered for this position is: $125,000 - $165,000